journeyend的专栏

One of the major drawbacks of modu- larized task-completion dialogue systems is that each module is trained individu- ally, which presents several challenges. For example, downstream modules are af- fected by earlier modules, and the per- formance of the entire system is not ro- bust to the accumulated errors. This pa- per presents a novel end-to-end learning framework for task-completion dialogue systems to tackle such issues. Our neu- ral dialogue system can directly interact with a structured database to assist users in accessing information and accomplish- ing certain tasks. The reinforcement learn- ing based dialogue manager offers robust capabilities to handle noises caused by other components of the dialogue system. Our experiments in a movie-ticket book- ing domain show that our end-to-end sys- tem not only outperforms modularized di- alogue system baselines for both objective and subjective evaluation, but also is ro- bust to noises as demonstrated by several systematic experiments with different er- ror granularity and rates specific to the lan- guage understanding module1.

In this paper, we propose a novel method called Rotational Region CNN (R2CNN) for detecting arbitrary-oriented texts in natural scene images. The framework is based on Faster R-CNN [1] architecture. First, we use the Region Proposal Network (RPN) to generate axis-aligned bounding boxes that enclose the texts with different orientations. Second, for each axis-aligned text box proposed by RPN, we extract its pooled features with different pooled sizes and the concatenated features are used to simultaneously predict the text/non-text score, axis-aligned box and inclined minimum area box. At last, we use an inclined non-maximum suppression to get the detection results. Our approach achieves competitive results on text detection benchmarks: ICDAR 2015 and ICDAR 2013.

HMM-based Script Identification for OCR While current OCR systems are able to recognize text in an increasing number of scripts and languages, typically they still need to be told in advance what those scripts and languages are. We propose an approach that repurposes the same HMM-based system used f